Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics· FDD-ON：用于VAV HVAC系统的故障检测和诊断本体
Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. However, effective deployment of FDD solutions in buildings requires structured domain knowledge that can bridge heterogeneous data sources, diverse equipment types, and varied diagnostic outputs. Limited data interpretability and interoperability within the FDD domain have led to fragmented information silos, hindering the implementation of FDD and related applications, such as the digital twin-enabled FDD frameworks and artificial intelligence (AI)-driven maintenance decision-making systems. This paper presents an FDD Ontology (FDD-ON), a modular and extensible ontology to formally represent variable air volume (VAV) HVAC system components,
论文提出了一种用于VAV HVAC系统的故障检测和诊断本体。
- 核心方法
- 开发了FDD-ON，一种模块化和可扩展的本体，用于正式表示VAV HVAC系统组件，旨在提高数据解释性和互操作性。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有FDD技术在建筑中的部署受限于缺乏结构化领域知识，导致数据解释性和互操作性差，信息孤岛问题严重。
- 关键实验
- 未提供
- 主要贡献
- 提供了结构化的VAV HVAC系统FDD领域知识，促进了不同数据源和设备类型的集成与应用。
- 意义与局限
- 有助于构建数字孪生FDD框架和AI驱动的维护决策系统，对提高HVAC系统的可靠性、能效和维护效果有重要影响；但可能受限于特定系统，适用性有待进一步验证。